A magnetic resonance imaging (“MRI”) device is an imaging device that uses powerful magnets and radio waves to create images of the body. In order to create these images, the MRI device, or its related software or hardware, must perform a process known as reconstruction. Reconstruction is a mathematical process that converts composite samples obtained by the MRI device into an image.
The quality of a reconstructed image is dependent, in part, on the number of samples used to generate the image. However, factors such as the physical constraints of MRI device make it desirable to provide high reconstruction quality for a limited number of samples. Sparse reconstruction algorithms such as the Fast Iterative Shrinkage-Thresholding Algorithm (“FISTA”) are designed to address this problem by reducing the number of samples required per temporal phase for a fixed quality level. However, such reconstruction algorithms are computationally intensive and require relatively long processing times. Thus, it would be desirable to address these computational requirements such that the benefits of sparse reconstruction algorithms may be leveraged, while the processing time required to reconstruct an image from a given number of MR samples is minimized.